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Unraveling fundamental properties of power system resilience curves using unsupervised machine learning

Energy and AI 2024年第2期16卷 227-240页

作者： Bo Li ali mostafavi Urban Resilience.AI Lab Zachry Department of Civil and Environmental EngineeringTexas A&M University3136 TAMU College StationTX77843-3136USA

Power system is vital to modern societies,while it is susceptible to hazard ***,analyzing resilience characteristics of power system is *** standard model of infrastructure resilience,the resilience triangle,has been the primary way of characterizing and quantifying resilience in infrastructure systems for more than two ***,the theoretical model provides a one-size-fits-all framework for all infrastructure systems and specifies general characteristics of resilience curves(e.g.,residual performance and duration of recovery).Little empirical work has been done to delineate infrastructure resilience curve archetypes and their fundamental properties based on observational *** of the existing studies examine the characteristics of infrastructure resilience curves based on analytical models constructed upon simulated system *** is a dire dearth of empirical studies in the field,which hindered our ability to fully understand and predict resilience characteristics in infrastructure *** address this gap,this study examined more than two hundred power-grid resilience curves related to power outages in three major extreme weather events in the United *** the use of unsupervised machine learning,we examined different curve archetypes,as well as the fundamental properties of each resilience curve *** results show two primary archetypes for power grid resilience curves,triangular curves,and trapezoidal *** curves characterize resilience behavior based on three fundamental properties:*** functionality threshold,*** functionality recovery rate,and *** pivot *** archetypes explain resilience curves based on *** of sustained function loss and *** recovery *** longer the duration of sustained function loss,the slower the constant rate of *** findings of this study provide novel perspectives enabling better understanding and predicti

关键词： Power system Infrastructure resilience Unsupervised learning Time-series clustering Resilience curve

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Inclusion of CoTiO_(3) to ameliorate the re/dehydrogenation properties of the mg–Na–Al system

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Journal of magnesium and Alloys 2024年第3期12卷 1215-1226页

作者： N.A.ali N.Y.Yusnizam N.A.Sazelee Sami-ullah Rather Haizhen Liu m.Ismail Energy Storage Research Group Faculty of Ocean Engineering TechnologyUniversiti Malaysia Terengganu21030 Kuala NerusTerengganuMalaysia Department of Chemical and materials Engineering King Abdulaziz UniversityP.O.Box80204Jeddah 21589Saudi Arabia Guangxi Novel Battery materials Research Center of Engineering Technology School of Physical Science and TechnologyGuangxi UniversityNanning 530004China State Key Laboratory of Featured metal materials and Life-Cycle Safety for Composite Structures Nanning 530004China

For the first time,the mgH_(2)–NaAlH_(4)(ratio 4:1)destabilized system with CoTiO_(3) addition has been *** CoTiO_(3)-doped mgH_(2)–NaAlH_(4) sample begins to dehydrogenate at 130℃,which is declined by 40℃ compared to the undoped mgH_(2)–NaAlH_(4).moreover,the de/rehydrogenation kinetics characteristics of the CoTiO_(3)-doped mgH_(2)–NaAlH_(4) were greatly *** the inclusion of CoTiO_(3),the mgH_(2)–NaAlH_(4) composite absorbed 5.2 wt.%H_(2),higher than undoped mgH_(2)–NaAlH_(4).In the context of dehydrogenation,the CoTiO_(3)-doped mgH_(2)–NaAlH_(4) sample desorbed 2.6 wt.%H_(2),almost doubled compared to the amount of hydrogen desorbed from the undoped mgH_(2)–NaAlH_(4) *** activation energy obtained by the Kissinger analysis for mgH_(2) decomposition was significantly lower by 35.9 kJ/mol than the undoped mgH_(2)–NaAlH_(4) *** reaction mechanism demonstrated that new phases of mgCo and AlTi_(3) were generated in situ during the heating process and are likely to play a substantial catalytic function and be useful in ameliorating the de/rehydrogenation properties of the destabilized mgH_(2)–NaAlH_(4) system with the inclusion of CoTiO_(3).

关键词： Destabilize system magnesium hydride Sodium alanate Hydrogen storage Additive.

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Convective drying of wood chips:Accelerating coupled DEm-CFD simulations with parametrized reduced single particle models

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Particuology 2024年第1期84卷 158-167页

作者： Lucas Reineking Jonas Fischer ali mjalled Enric Illana Siegmar Wirtz Viktor Scherer martin monnigmann Automatic Control and System Theory Ruhr-Universität-BochumUniversitätstraße 150Bochum44801Germany Energy Plant Technology Ruhr-Universität-BochumUniversitätstraße 150Bochum44801Germany

The simulation of industry-scale reactive bulks is challenging due to the complex interaction between fluid and *** particles in the bulk and their interaction with the fluid flow can be described by combined Discrete Element method-Computational Fluid Dynamics(DEm-CFD)***,the computational cost of the Finite Volume(FV)methods deployed in these models can become prohibi-tively expensive,especially for high inner-particle *** particle Reduced models(Rms)can be used to achieve both fast and accurate descriptions of the processes in each *** an example of bulk systems comprising heat and mass transfer,we compared FV and Rm simulations for the drying of wood chips in a bulk reactor.A manifold-based nonlinear interpolation was applied to resolve changing boundary conditions for the *** simulations showed that Rms provide accurate values for the thermodynamic state variables of the ***,the time required for the bulk simulation was reduced by 67%with the *** is evident that simulations with high inner-particle resolution can be accelerated by Rms if manifold-based nonlinear interpolation is used to address changing boundary conditions.

关键词： Reduced model Reactive bulk Discrete element method manifold-based interpolation Drying of porous mediaDEm-CFD

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NeuroPNm:model reduction of pore network models using neural networks

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Particuology 2024年第3期86卷 239-251页

作者： Robert Jendersie ali mjalled Xiang Lu Lucas Reineking Abdolreza Kharaghani martin monnigmann Christian Lessig Otto-von-Guericke-Universitat magdeburg Universitatsplatz 2Magdeburg39106Germany Ruhr-Universitat-Bochum Universitatstraβe 150Bochum44801Germany

Reacting particle systems play an important role in many industrial applications,for example biomass drying or the manufacturing of *** numerical modeling and simulation of such systems is therefore of great importance for an efficient,reliable,and environmentally sustainable operation of the *** complex thermodynamical,chemical,and flow processes that take place in the particles are a particular challenge in a ***,typically a large number of particles is involved,rendering an explicit treatment of individual ones impossible in a reactor-level *** approach for overcoming this challenge is to compute effective,physical parameters from single-particle,high-resolution *** can be combined with model reduction methods if the dynamical behaviour of particles must be *** network models with their unrivaled resolution have thereby been used successfully as high-resolution models,for instance to obtain the macroscopic diffusion coeffcient of *** parameter identification and model reduction have recently gained new impetus by the dramatic progress made in machine learning in the last *** report results on the use of neural networks for parameter identification and model reduction based on three-dimensional pore network models(PNm).We believe that our results provide a powerful complement to existing methodologies for reactor-level simulations with many thermally-thick particles.

关键词： Porenetwork models Neural networks Parameter estimation Reduced order model

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Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

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Computers, materials & Continua 2024年第7期80卷 1163-1177页

作者： Amel ali Alhussan Doaa Sami Khafaga El-Sayed m.El-kenawy marwa m.Eid Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and TechnologyMansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric *** demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of *** establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this ***,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of *** the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging *** is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic *** vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering ***,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature ***,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic *** on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is *** results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algori

关键词： Electric vehicle graylag goose optimization metaheuristics optimization machine learning

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Development of a Novel Noise Reduction Algorithm for Smart Checkout RFID System in Retail Stores

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Computers, materials & Continua 2024年第7期80卷 1277-1304页

作者： Shazielya Shamsul mohammed A.H.ali Salwa Hanim Abdul-Rashid Atef m.Ghaleb Fahad m.Alqahtani Department of mechanical Engineering Faculty of EngineeringUniversiti MalayaKuala Lumpur50603Malaysia Centre for Sustainable and Smart manufacturing(CSSm) Faculty of EngineeringUniversiti MalayaKuala Lumpur50603Malaysia Department of Industrial Engineering College of EngineeringAlfaisal UniversityRiyadh11533Saudi Arabia Department of Industrial Engineering College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia

This paper presents a smart checkout system designed to mitigate the issues of noise and errors present in the existing barcode and RFID-based systems used at retail stores’checkout *** is achieved by integrating a novel AI algorithm,called Improved Laser Simulator Logic(ILSL)into the RFID *** enhanced RFID system was able to improve the accuracy of item identification,reduce noise interference,and streamline the overall checkout *** potential of the systemfor noise detection and elimination was initially investigated through a simulation study usingmATLAB and ILSL ***,it was deployed in a small-scale environment to validate its real-world *** show that RFID with the proposed new algorithm ILSL and AI basket is capable of accurately detecting the related itemswhile eliminating noise originating fromunrelated objects,achieving an accuracy rate of 88%.

关键词： Smart checkout system RFID technology improved laser simulator logic(ILSL) reduce noise checkout process

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Analysis of cohesive particles mixing behavior in a twin-paddle blender:DEm and machine learning applications

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Particuology 2024年第7期90卷 350-363页

作者： Behrooz Jadidi mohammadreza Ebrahimi Farhad Ein-mozaffari ali Lohi Department of Chemical Engineering Toronto Metropolitan University350 Victoria StreetToronto M5B 2K3Canada

This research paper presents a comprehensive discrete element method(DEm)examination of the mixing behaviors exhibited by cohesive particles within a twin-paddle blender.A comparative analysis between the simulation and experimental results revealed a relative error of 3.47%,demonstrating a strong agreement between the results from the experimental tests and the DEm *** main focus centers on systematically exploring how operational parameters,such as impeller rotational speed,blender's fill level,and particle mass ratio,influence the *** investigation also illustrates the significant influence of the mixing time on the mixing *** gain a deeper understanding of the DEm simulation findings,an analytical tool called multivariate polynomial regression in machine learning is *** method uncovers significant connections between the DEm results and the operational parameters,providing a more comprehensive insight into their *** multivariate polynomial regression model exhibited robust predictive performance,with a mean absolute percentage error of less than 3%for both the training and validation sets,indicating a slight deviation from actual *** model's precision was confirmed by low mean absolute error values of 0.0144(80%of the dataset in the training set)and 0.0183(20%of the dataset in the validation set).The study offers valuable insights into granular mixing behaviors,with implications for enhancing the efficiency and predictability of the mixing processes in various industrial applications.

关键词： machine learning Granular mixing Discrete element method mixing kinetics and mechanism Cohesive particles

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Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

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Computer modeling in Engineering & Sciences 2024年第8期140卷 1539-1562页

作者： Samar m.Alqhtani Toufique A.Soomro Faisal Bin Ubaid Ahmed ali muhammad Irfan Abdullah A.Asiri Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Electronic Engineering The University of LarkanoLarkanaSindh75660Pakistan Computer Science Department Sukkur IBA UniversitySukkurSindh65200Pakistan Eletrical Engineering Department Sukkur IBA UniversitySukkurSindh65200Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various *** resonance imaging(mRI)and computed tomography(CT)are utilized to capture brain *** plays a crucial role in the diagnosis of brain tumors and the examination of other brain ***,manual assessment of mRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely ***,early diagnosis of brain tumors is intricate,necessitating the use of computerized *** research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain *** proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-means(FCm)clustering technique in the third *** final step involves classification using the Support Vector machine(SVm)*** classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced magnetic Resonance Imaging(CE-mRI)*** proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of ***,this method exhibits a shorter processing time of 0.44 s compared to existing *** performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and *** enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classi

关键词： Brain tumor magnetic resonance imaging principal component analysis fuzzy c-clustering support vector machine

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Smart Energy management System Using machine Learning

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Computers, materials & Continua 2024年第1期78卷 959-973页

作者： ali Sheraz Akram Sagheer Abbas muhammad Adnan Khan Atifa Athar Taher m.Ghazal Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy management System(IEmS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(mL)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEmS is proposed for Smart Cities(SC)using the mL technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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Ultrafast magneto-optical dynamics in nickel(111)single crystal studied by the integration of ultrafast reflectivity and polarimetry probes

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Chinese Physics B 2024年第3期33卷 65-69页

作者：匡皓余军潇陈洁 H.E.Elsayed-ali 李润泽 Peter m.Rentzepis School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China Center for Transformative Science ShanghaiTech UniversityShanghai 201210China Center for Ultrafast Science and Technology Key Laboratory for Laser Plasmas (Ministry of Education)Collaborative Innovation Center of IFSA (CICIFSA)School of Physics and AstronomyShanghai Jiao Tong UniversityShanghai 200240China Department of Electrical and Computer Engineering Old Dominion UniversityNorfolkVirginia 23529USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTexas 77843USA

With the integration of ultrafast reflectivity and polarimetry probes,we observed carrier relaxation and spin dynamics induced by ultrafast laser excitation of Ni(111)single *** carrier relaxation time within the linear excitation range reveals that electron-phonon coupling and dissipation of photon energy into the bulk of the crystal take tens of *** the other hand,the observed spin dynamics indicate a longer time of about 120 *** further understand how the lattice degree of freedom is coupled with these dynamics may require the integration of an ultrafast diffraction probe.

关键词： ultrafast spin dynamics non-equilibrium dynamics multi-probe

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